Upgrade to Pro
— share decks privately, control downloads, hide ads and more …
Speaker Deck
Features
Speaker Deck
PRO
Sign in
Sign up for free
Search
Search
突然ですが「生涯成績」占ってもいいですか? - プロ野球選手成績予測2022
Search
Shinichi Nakagawa
PRO
April 02, 2022
Research
0
350
突然ですが「生涯成績」占ってもいいですか? - プロ野球選手成績予測2022
とあるLT会で雑に話したプロ野球選手成績予測ネタ
#機械学習 #BigQuery #Python #BIGBOSS #北海道日本ハムファイターズ
Shinichi Nakagawa
PRO
April 02, 2022
Tweet
Share
More Decks by Shinichi Nakagawa
See All by Shinichi Nakagawa
生成AI時代におけるSREの進化とキャリア戦略 / Building an Embedded SRE team and my career
shinyorke
PRO
0
100
生成AIを活用した野球データ分析 - メジャーリーグ編 / Baseball Analytics for Gen AI
shinyorke
PRO
1
5k
ゼロから始めるSREの事業貢献 - 生成AI時代のSRE成長戦略と実践 / Starting SRE from Day One
shinyorke
PRO
2
4.8k
AI・LLM事業部のSREとタスクの自動運転
shinyorke
PRO
0
470
実践Dash - 手を抜きながら本気で作るデータApplicationの基本と応用 / Dash for Python and Baseball
shinyorke
PRO
2
3.6k
Terraform, GitHub Actions, Cloud Buildでデータ基盤をProvisioningする / Data Platform provisioning for Google Cloud and Terraform
shinyorke
PRO
2
3.4k
Cloud RunとCloud PubSubでサーバレスなデータ基盤2024 with Terraform / Cloud Run and PubSub with Terraform
shinyorke
PRO
9
4.2k
自らを強いエンジニアにするための3つの習慣 / I need to be myself, I can't be no one else
shinyorke
PRO
85
88k
阪神タイガース優勝のひみつ - Pythonでシュッと調べた件 / SABRmetrics for Python
shinyorke
PRO
1
1.5k
Other Decks in Research
See All in Research
A scalable, annual aboveground biomass product for monitoring carbon impacts of ecosystem restoration projects
satai
4
350
Time to Cash: The Full Stack Breakdown of Modern ATM Attacks
ratatata
0
160
[RSJ25] Enhancing VLA Performance in Understanding and Executing Free-form Instructions via Visual Prompt-based Paraphrasing
keio_smilab
PRO
0
140
IMC の細かすぎる話 2025
smly
2
690
ロボット学習における大規模検索技術の展開と応用
denkiwakame
1
130
情報技術の社会実装に向けた応用と課題:ニュースメディアの事例から / appmech-jsce 2025
upura
0
200
とあるSREの博士「過程」 / A Certain SRE’s Ph.D. Journey
yuukit
11
4.4k
Remote sensing × Multi-modal meta survey
satai
4
490
Unsupervised Domain Adaptation Architecture Search with Self-Training for Land Cover Mapping
satai
3
210
PhD Defense 2025: Visual Understanding of Human Hands in Interactions
tkhkaeio
1
250
Mechanistic Interpretability:解釈可能性研究の新たな潮流
koshiro_aoki
1
460
ip71_contraflow_reconfiguration
stkmsd
0
110
Featured
See All Featured
Creating an realtime collaboration tool: Agile Flush - .NET Oxford
marcduiker
33
2.3k
Bootstrapping a Software Product
garrettdimon
PRO
307
110k
Save Time (by Creating Custom Rails Generators)
garrettdimon
PRO
32
1.6k
The Straight Up "How To Draw Better" Workshop
denniskardys
238
140k
Evolution of real-time – Irina Nazarova, EuRuKo, 2024
irinanazarova
9
980
Building Adaptive Systems
keathley
44
2.8k
The Pragmatic Product Professional
lauravandoore
36
6.9k
VelocityConf: Rendering Performance Case Studies
addyosmani
332
24k
Building Better People: How to give real-time feedback that sticks.
wjessup
369
20k
Bash Introduction
62gerente
615
210k
"I'm Feeling Lucky" - Building Great Search Experiences for Today's Users (#IAC19)
danielanewman
230
22k
RailsConf 2023
tenderlove
30
1.2k
Transcript
ಥવͰ͕͢ʮੜ֔ʯ͍͍ͬͯͰ͔͢? Shinichi Nakagawa(@shinyorke)
͋ɺٿબखͷʮੜ֔ʯͰ͢Α
ຊͷࢼ߹ • Python + BigQueryͰͪΐͬͱͨ͠ػցֶशΛΔํ๏ • ࠓϓϩٿͰ͍ͨ͠एखબखͷઌΛ͏ • BIG BOSSͱ(ry
ࣗݾհ • Shinichi Nakagawaʢத ৳Ұʣ • WebܥϑϧαΠΫϧɾΤϯδχΞʢML, Backend, SRE, Frontendʣ
• झຯ͓ΑͼݩͷࣄʮٿΤϯδχΞ݉σʔλαΠΤϯςΟετʯ • Python, Google Cloud, ٿ౷ܭֶͷਓ • ৽ঙ߶ࢤͷϓϨʔΛ͖͔͚ͬʹٿϑΝϯʹͳͬͨ
ϓϩٿબखͷ༧ଌΛ ͍͍ײ͡ʹߦ͏ํ๏ʢμΠδΣετʣ
ΞʔΩςΫνϟʢ֓ཁʣ σʔλΛͱΓ͋͑ͣBigQueryʹೖΕΔ, ੳJupyterLab WebΞϓϦʹ͢ΔͳΒStreamlitͰ͍͍ײ͡ʹग़དྷ·͢
ϓϩٿબखͷ༧ଌϞσϧ 1.༧ଌ͍ͨ͠બखʹࣅ͍ͯΔબखΛۙࣅ࠷ۙ୳ࡧͰநग़ ʢ௨ࢉΛಛྔʹ͍ͯۙ͠͠બखΛϐοΫΞοϓʣ 2.↑Ͱग़ͨ͠ࣅ͍ͯΔબखͷྸຖͷΛग़͢ 3.↑ͷྸຖฏۉΛ༧ଌͱͯ͠ѻ͏ ϝδϟʔϦʔάͷެ։σʔληοτΛར༻ʢͯ͢ӳޠʣ
ֆʹ͢Δͱ͜͏͍͏ྲྀΕ. ۩ମతͳߟ͑ํɾΞϓϩʔνPyCon JP 2020Ͱ͓Λͨ͠ͷͰͦͪΒΛ͝ཡ͍ͩ͘͞. https://shinyorke.hatenablog.com/entry/baseball-and-ml-with-python
ඵͰղઆʮ2022ϓϩٿͷݟͲ͜Ζʯ • BIG BOSSര • ࡳຈυʔϜ, ࠷ޙͷγʔζϯʢຊڌͱͯ͠ʣ • ݱબखυϥϑτɹ˞ະఆͰ͕͢ಋೖͷՄೳੑ͋Γ㽂 •
ϑϨογϡͳएखબखͷ׆༂ʢ༧ఆʣ
ϑϨογϡͳएखબखͷ׆༂Λ༧ • ࢲ͕ਪ͍ͯ͠ΔւಓຊϋϜϑΝΠλʔζͷएखબख • ͷճΓͷಉ྅Β༑ਓΒ͔ΒϦΫΤετ͕͋ͬͨएखબख • ͜ΕΒΛݩʹ, ʮएͯ͘কདྷ׆༂ͦ͠͏ʯͳબखͷΛ༧
AIͰ͏ʮظͷएखϓϩٿબखʯ • ԣDeNAϕΠελʔζظͷγϣʔτʮ ܟేʯ • ౡ౦༸ΧʔϓظͷεϐʔυελʔʮӉ جʯ • BIG BOSSʹ࠷͍ۙͷϑΟδΧϧϞϯελʔʮສ
தਖ਼ʯ 5ઌͷଧɾώοτɾຊྥଧɾଧΛ༧ଌ͠·ͨ͠
Ұਓʮ ܟేʢΓ ͚͍ͱʣʯ • ԣDeNAϕΠελʔζɾख • 2019υϥϑτ1ҐʢۅӂֶԂߴʣ • ମೳྗൈ܈ͷγϣʔτ, ԣظͷ
5ͨͳ͍ͱ͍͚ͳ͍ͬΆ͍ʢখʣ
ܟేͷ༧ଌ • ࠓͷ༧ʮଧ.211 ຊྥଧ6ຊ ଧ37ʯ • ଧ͕͜ΕͰ6ຊϗʔϜϥϯଧͯͨΒٯʹظͰ͖ͦ͏ • 5ޙʮଧ.286
ຊྥଧ6ຊ ଧ50ʯͳͷͰϙδΕͦ͏
ೋਓʮӉ جʢ͏͙͞ ͜͏͖ʣʯ • ౡ౦༸Χʔϓɾ֎ख • 2019υϥϑτ2Ґʢ๏େֶʣ • εϐʔυ͕ചΓͷ֎ख, MLBʹҠ੶ͨ͠ླͷޙ佂ީิ
͜ͷ, Ϡό͘ͳ͍Ͱ͔͢ʢ͑ʣ
Ӊ جͷ༧ଌ • ࠓͷ༧ʮଧ.262 ຊྥଧ33ຊ ଧ87ʯ • ͜Εྲྀੴʹग़དྷ͗͢Ͱ🤔ϗϯτʹ࣮ݱͨ͠ΒදϨϕϧ • 5ޙʮଧ.294
ຊྥଧ24ຊ ଧ86ʯϦΞϧʹग़ͦ͠͏
ࡾਓʮສ தਖ਼ʢ·ΜͳΈ ͪΎ͏͍ͤʣʯ • ւಓຊϋϜϑΝΠλʔζɾ֎ख • 2018υϥϑτ4Ґʢԣߴߍʣ • ύϫʔͱεϐʔυ, ࡶ͞Λ݉Ͷἧ͑ͨϑΟδΧϧϞϯελʔ
ϓϨʔελΠϧݱ࣌ͷBIG BOSSʹඇৗʹ͍ۙ
͜ΕϦΞϦςΟᷓΕΔ
ສ தਖ਼ͷ༧ଌ • ࠓͷ༧ʮଧ.250 ຊྥଧ26ຊ ଧ66ʯ • ελϝϯػձ૿͑ͨΒΓͦ͏ͳࣈ • 5ޙʮଧ.254
ຊྥଧ23ຊ ଧ77ʯ BIG BOSSͷݱ࣌ͬΆ͍ࣈͳΜͰ͢Α͜ͷงғؾ
ສ தਖ਼ͱBIG BOSS ଧ ຊྥଧ ଧ #*(#044ࡀ ʢࡕਆ࣌ʣ
ຊ ଧ ສதਖ਼ࡀ ʢͷ༧ଌʣ ຊ ଧ ΊͬͪΌࣅͯ·ͤΜ͔?
͖͏AIͰBIG BOSSͷޙܧऀ, ݟ͚ͭ·ͨ͠ʢ͜ͳΈʣ
ήʔϜηοτ⚾ ͝ਗ਼ௌ͋Γ͕ͱ͏͍͟͝·ͨ͠. Shinichi Nakagawa(Twitter/Facebook/etc… @shinyorke)